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Knowledge representation and reasoning (2003)
| Citations: | 198 - 3 self |
BibTeX
@MISC{Brachman03knowledgerepresentation,
author = {Ronald J. Brachman and Hector J. Levesque},
title = {Knowledge representation and reasoning},
year = {2003}
}
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Abstract
Knowledge Representation is the area of Artificial Intelligence (AI) concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. It is at the very core of a radical idea about how to understand intelligence: instead of trying to understand or build brains from the bottom up, we try to understand or build intelligent behavior from the top down. In particular, we ask what an agent would need to know in order to behave intelligently, and what computational mechanisms could allow this knowledge to be made available to the agent as required. This book is intended as a text for an introductory course in this area of research. There are many different ways to approach and study the area of Knowledge Representation. One might think in terms of a representation language like that of symbolic logic, and concentrate on how logic can be applied to problems in AI. This has led to courses and research in what is sometimes called “logic-based AI.” In a different vein, it is possible to study Knowledge Representation in terms of the specification and development of large knowledge-based systems. From this
